Utilizing Drones to Restore and Maintain Radio Communication During Search and Rescue Operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The ability of rescuers to maintain contact with incident command (IC) and each other is a critical component of search and rescue (SAR) operations. When rescuers lose radio communication with operation leaders, the effectiveness of operations may be substantially affected. This often occurs owing to the limitations of standard communications equipment in difficult terrain or when victims are beyond line-of-sight. This study investigates the viability of using an aerial drone-repeater system configuration to restore and maintain radio communications between IC and deployed rescuers. METHODS: SAR operators in Southern Utah identified 10 areas where radio communication is compromised during live rescue operations. Trained SAR personnel were deployed to these areas in a mock exercise. After confirmed loss of communication, a repeater-equipped aerial drone was piloted 122 m above IC to restore communication. Once restored, communication was assessed at regular intervals for the duration of the mock deployment. RESULTS: In all 10 areas tested, communication was successfully restored. In all cases, once communication was restored, no additional loss of radio contact occurred. The time between communication loss and restoration across the 10 scenarios was 6.5±1.1 (4.4-9.3) min (mean±SD with range). CONCLUSIONS: This method of restoring radio communication among SAR personnel could drastically improve the ability to assist victims and help mitigate the risks faced by rescuers. SAR leaders should be made aware of the useful applications of drones during SAR operations, especially in instances where communication is compromised.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it